Delete model.py

model.py

@@ -1,63 +0,0 @@
-from transformers import PreTrainedModel
-import torch
-
-from nugget_model_utils import CustomRobertaWithPOS as NuggetModel
-from args_model_utils import CustomRobertaWithPOS as ArgumentModel
-from realis_model_utils import CustomRobertaWithPOS as RealisModel
-
-from event_nugget_predict import create_dataloader as event_nugget_dataloader
-from event_realis_predict import create_dataloader as event_realis_dataloader
-from event_arg_predict import create_dataloader as event_argument_dataloader
-
-class CybersecurityKnowledgeGraphModel(PreTrainedModel):
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.event_nugget_model_path = config.event_nugget_model_path
-        self.event_argument_model_path = config.event_argument_model_path
-        self.event_realis_model_path = config.event_realis_model_path
-
-        self.event_nugget_dataloader = event_nugget_dataloader
-        self.event_argument_dataloader = event_argument_dataloader
-        self.event_realis_dataloader = event_realis_dataloader
-
-        self.event_nugget_model = NuggetModel(num_classes = 11)
-        self.event_argument_model = ArgumentModel(num_classes = 43)
-        self.event_realis_model = RealisModel(num_classes_realis = 4)
-
-        self.event_nugget_model.load_state_dict(torch.load(self.event_nugget_model_path))
-        self.event_realis_model.load_state_dict(torch.load(self.event_realis_model_path))
-        self.event_argument_model.load_state_dict(torch.load(self.event_argument_model_path))
-
-
-    def forward(self, text):
-        nugget_dataloader, _ = self.event_nugget_dataloader(text)
-        argument_dataloader, _ = self.event_argument_dataloader(text)
-        realis_dataloader, _ = self.event_realis_dataloader(text)
-
-        nugget_pred = self.forward_model(self.event_nugget_model, nugget_dataloader)
-        no_nuggets = torch.all(nugget_pred == 0, dim=1)
-
-        argument_preds = torch.empty(nugget_pred.size())
-        realis_preds = torch.empty(nugget_pred.size())
-        for idx, (batch, no_nugget) in enumerate(zip(nugget_pred, no_nuggets)):
-            if no_nugget:
-                argument_pred, realis_pred = torch.zeros(batch.size()), torch.zeros(batch.size())
-            else:
-                argument_pred = self.forward_model(self.event_argument_model, argument_dataloader)
-                realis_pred = self.forward_model(self.event_realis_model, realis_dataloader)
-            argument_preds[idx] = argument_pred
-            realis_preds[idx] = realis_pred
-
-        return {"nugget" : nugget_pred, "argument" : argument_pred, "realis" : realis_pred}
-
-    def forward_model(self, model, dataloader):
-        predicted_label = []
-        for batch in dataloader:
-            with torch.no_grad():
-                print(batch.keys())
-                logits = model(**batch)
-
-            batch_predicted_label = logits.argmax(-1)
-            predicted_label.append(batch_predicted_label)
-        return torch.cat(predicted_label, dim=-1)